Follow the data workflow: collect and clean data, explore datasets, define problems, apply models, and communicate findings through data storytelling.

Understand the basics of data analysis, like using Python or R for statistical analysis, creating data visualizations, and interpreting results.

Apply foundational data science concepts, like supervised vs. unsupervised learning, model evaluation, and ethical data usage.

Create a professional data science portfolio, including end-to-end projects with real-world datasets, so that you're ready to apply for jobs.